{"id":318,"date":"2025-05-27T11:35:07","date_gmt":"2025-05-27T15:35:07","guid":{"rendered":"https:\/\/www.clarku.edu\/data-science-program\/?p=318"},"modified":"2025-06-05T15:39:58","modified_gmt":"2025-06-05T19:39:58","slug":"dsci-215-applying-deep-learning-to-earth-observation","status":"publish","type":"post","link":"https:\/\/www.clarku.edu\/data-science\/dsci-215-applying-deep-learning-to-earth-observation\/","title":{"rendered":"DSCI 215 – Applying Deep Learning to Earth Observation"},"content":{"rendered":"\n

Focusing on convolutional and recurrent neural networks, this course will provide an in-depth overview of key machine learning algorithms and their application to satellite imagery (especially for the task of semantic segmentation), including the full workflow required to acquire and process imagery, develop and train a model, and make and critically evaluate the resulting maps. The course will be strongly hands-on, and emphasize the use of programming (python), open image archives and EO analytical platforms (e.g. Google Earth Engine), and tools for creating open and reproducible workflows (git and GitHub).<\/p>\n\n\n\n

For DSCI students, this course satisfies the capstone requirement.

Prerequisites:<\/strong> There are two sets of prerequisites for this course, one to ensure that students have sufficient background in GIScience and the other to ensure that they have enough programming experience.<\/p>\n\n\n\n

GIScience Prereqs:
GEOG 293<\/a> or GEOG 383<\/a> with preference given to students that have taken GEOG 282<\/a> or GEOG 382<\/a> and GEOG 296<\/a> or GEOG 397<\/a> <\/p>\n\n\n\n

Programming Prereqs:
Can be satisfied through two approaches –<\/p>\n\n\n\n

Approach 1 – take at least one: DSCI 105<\/a>\u00a0or DSCI 305<\/a>; DSCI 125<\/a>\u00a0or DSCI 304<\/a>; CSCI 120<\/a>\u00a0or CSCI 301<\/a>\u00a0<\/p>\n\n\n\n

Approach 2 – take at least two: SSJ 302<\/a>; SSJ 30274<\/a>; GEOG 246<\/a>\u00a0or GEOG 346<\/a>\u00a0<\/p>\n\n\n\n

Determination regarding whether prerequisites can be waived or satisfied by other means may also be made by the instructors.

Anticipated Terms Offered:<\/strong> Annually<\/p>\n","protected":false},"excerpt":{"rendered":"

Applying Deep Learning to Earth Observation delves into the application of deep learning techniques to analyze and interpret data from Earth observation systems.<\/p>\n","protected":false},"author":7,"featured_media":319,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[21],"tags":[],"class_list":["post-318","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-science-core"],"yoast_head":"\nDSCI 215 - Applying Deep Learning to Earth Observation | Data Science | Clark University<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.clarku.edu\/data-science\/dsci-215-applying-deep-learning-to-earth-observation\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"DSCI 215 - 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